{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Will your machine be hit by a malware soon?\n",
    "Malware, or malicious software, is any program or file that is harmful to a computer user. Malware includes computer viruses, worms, Trojan horses and spyware. These malicious programs can perform a variety of functions, including stealing, encrypting or deleting sensitive data, altering or hijacking core computing functions and monitoring users' computer activity without their permission. Once a machine is infected by malware, criminals can hurt consumers and enterprises in many ways. Cybercrimes have been increasing and the global cost of cybercrime has now reached as much as 600 billion dollars — about 0.8 percent of global GDP. <br> <br>\n",
    "\n",
    "To minimize risk, is it possible to predict the chance of machine getting infected using machine learning? <br>\n",
    "Let us look at a dataset provided by Microsoft 1 and see if we can use the power of machine learning to predict risk of a machine getting infected by malware. <br>\n",
    "## Table of Contents <br>\n",
    "<li>Descriptive Statistics</li>\n",
    "<li>Exploratory data analysis</li>\n",
    "<li>Feature engineering</li>\n",
    "<li>Summary</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "from IPython.display import HTML\n",
    "import IPython.core.display as di # Example: di.display_html('<h3>%s:</h3>' % str, raw=True)\n",
    "\n",
    "# This line will hide code by default when the notebook is exported as HTML\n",
    "di.display_html('<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}});</script>', raw=True)\n",
    "\n",
    "# This line will add a button to toggle visibility of code blocks, for use with the HTML export version\n",
    "di.display_html('''<button onclick=\"jQuery('.input_area').toggle(); jQuery('.prompt').toggle();\">Toggle code</button>''', raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the required libraries\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import KFold, metrics\n",
    "import warnings,gc,time,sys,datetime\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn import metrics\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We set the types of each fields in the train set in order to reduce the memory usage\n",
    "dtypes = {\n",
    "        'MachineIdentifier':                                    'category',\n",
    "        'ProductName':                                          'category',\n",
    "        'EngineVersion':                                        'category',\n",
    "        'AppVersion':                                           'category',\n",
    "        'AvSigVersion':                                         'category',\n",
    "        'IsBeta':                                               'int8',\n",
    "        'RtpStateBitfield':                                     'float16',\n",
    "        'IsSxsPassiveMode':                                     'int8',\n",
    "        'DefaultBrowsersIdentifier':                            'float16',\n",
    "        'AVProductStatesIdentifier':                            'float32',\n",
    "        'AVProductsInstalled':                                  'float16',\n",
    "        'AVProductsEnabled':                                    'float16',\n",
    "        'HasTpm':                                               'int8',\n",
    "        'CountryIdentifier':                                    'int16',\n",
    "        'CityIdentifier':                                       'float32',\n",
    "        'OrganizationIdentifier':                               'float16',\n",
    "        'GeoNameIdentifier':                                    'float16',\n",
    "        'LocaleEnglishNameIdentifier':                          'int8',\n",
    "        'Platform':                                             'category',\n",
    "        'Processor':                                            'category',\n",
    "        'OsVer':                                                'category',\n",
    "        'OsBuild':                                              'int16',\n",
    "        'OsSuite':                                              'int16',\n",
    "        'OsPlatformSubRelease':                                 'category',\n",
    "        'OsBuildLab':                                           'category',\n",
    "        'SkuEdition':                                           'category',\n",
    "        'IsProtected':                                          'float16',\n",
    "        'AutoSampleOptIn':                                      'int8',\n",
    "        'PuaMode':                                              'category',\n",
    "        'SMode':                                                'float16',\n",
    "        'IeVerIdentifier':                                      'float16',\n",
    "        'SmartScreen':                                          'category',\n",
    "        'Firewall':                                             'float16',\n",
    "        'UacLuaenable':                                         'float32',\n",
    "        'Census_MDC2FormFactor':                                'category',\n",
    "        'Census_DeviceFamily':                                  'category',\n",
    "        'Census_OEMNameIdentifier':                             'float16',\n",
    "        'Census_OEMModelIdentifier':                            'float32',\n",
    "        'Census_ProcessorCoreCount':                            'float16',\n",
    "        'Census_ProcessorManufacturerIdentifier':               'float16',\n",
    "        'Census_ProcessorModelIdentifier':                      'float16',\n",
    "        'Census_ProcessorClass':                                'category',\n",
    "        'Census_PrimaryDiskTotalCapacity':                      'float32',\n",
    "        'Census_PrimaryDiskTypeName':                           'category',\n",
    "        'Census_SystemVolumeTotalCapacity':                     'float32',\n",
    "        'Census_HasOpticalDiskDrive':                           'int8',\n",
    "        'Census_TotalPhysicalRAM':                              'float32',\n",
    "        'Census_ChassisTypeName':                               'category',\n",
    "        'Census_InternalPrimaryDiagonalDisplaySizeInInches':    'float16',\n",
    "        'Census_InternalPrimaryDisplayResolutionHorizontal':    'float16',\n",
    "        'Census_InternalPrimaryDisplayResolutionVertical':      'float16',\n",
    "        'Census_PowerPlatformRoleName':                         'category',\n",
    "        'Census_InternalBatteryType':                           'category',\n",
    "        'Census_InternalBatteryNumberOfCharges':                'float32',\n",
    "        'Census_OSVersion':                                     'category',\n",
    "        'Census_OSArchitecture':                                'category',\n",
    "        'Census_OSBranch':                                      'category',\n",
    "        'Census_OSBuildNumber':                                 'int16',\n",
    "        'Census_OSBuildRevision':                               'int32',\n",
    "        'Census_OSEdition':                                     'category',\n",
    "        'Census_OSSkuName':                                     'category',\n",
    "        'Census_OSInstallTypeName':                             'category',\n",
    "        'Census_OSInstallLanguageIdentifier':                   'float16',\n",
    "        'Census_OSUILocaleIdentifier':                          'int16',\n",
    "        'Census_OSWUAutoUpdateOptionsName':                     'category',\n",
    "        'Census_IsPortableOperatingSystem':                     'int8',\n",
    "        'Census_GenuineStateName':                              'category',\n",
    "        'Census_ActivationChannel':                             'category',\n",
    "        'Census_IsFlightingInternal':                           'float16',\n",
    "        'Census_IsFlightsDisabled':                             'float16',\n",
    "        'Census_FlightRing':                                    'category',\n",
    "        'Census_ThresholdOptIn':                                'float16',\n",
    "        'Census_FirmwareManufacturerIdentifier':                'float16',\n",
    "        'Census_FirmwareVersionIdentifier':                     'float32',\n",
    "        'Census_IsSecureBootEnabled':                           'int8',\n",
    "        'Census_IsWIMBootEnabled':                              'float16',\n",
    "        'Census_IsVirtualDevice':                               'float16',\n",
    "        'Census_IsTouchEnabled':                                'int8',\n",
    "        'Census_IsPenCapable':                                  'int8',\n",
    "        'Census_IsAlwaysOnAlwaysConnectedCapable':              'float16',\n",
    "        'Wdft_IsGamer':                                         'float16',\n",
    "        'Wdft_RegionIdentifier':                                'float16',\n",
    "        'HasDetections':                                        'int8'\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We have two files train and test, we will performing the training first and check our prediction model on test file.\n",
    "train = pd.read_csv('train_sample.csv', dtype=dtypes)\n",
    "test = pd.read_csv('test_sample.csv', dtype=dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a huge dataset with 84 attributes. The goal of this dataset is to predict a Windows machine’s probability of getting infected by various families of malware, based on different properties of that machine. The data containing these properties and the machine infections was generated by combining heartbeat and threat reports collected by Microsoft's endpoint protection solution, Windows Defender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.set_index('Unnamed: 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MachineIdentifier</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>EngineVersion</th>\n",
       "      <th>AppVersion</th>\n",
       "      <th>AvSigVersion</th>\n",
       "      <th>IsBeta</th>\n",
       "      <th>RtpStateBitfield</th>\n",
       "      <th>IsSxsPassiveMode</th>\n",
       "      <th>DefaultBrowsersIdentifier</th>\n",
       "      <th>AVProductStatesIdentifier</th>\n",
       "      <th>...</th>\n",
       "      <th>Census_FirmwareVersionIdentifier</th>\n",
       "      <th>Census_IsSecureBootEnabled</th>\n",
       "      <th>Census_IsWIMBootEnabled</th>\n",
       "      <th>Census_IsVirtualDevice</th>\n",
       "      <th>Census_IsTouchEnabled</th>\n",
       "      <th>Census_IsPenCapable</th>\n",
       "      <th>Census_IsAlwaysOnAlwaysConnectedCapable</th>\n",
       "      <th>Wdft_IsGamer</th>\n",
       "      <th>Wdft_RegionIdentifier</th>\n",
       "      <th>HasDetections</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1713700</th>\n",
       "      <td>312ee21c55c435e926e8d697463cb3b0</td>\n",
       "      <td>win8defender</td>\n",
       "      <td>1.1.15200.1</td>\n",
       "      <td>4.18.1806.18062</td>\n",
       "      <td>1.275.202.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53447.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64689.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>win8defender</td>\n",
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       "      <td>4.18.1807.18075</td>\n",
       "      <td>1.275.1141.0</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47380.0</td>\n",
       "      <td>...</td>\n",
       "      <td>51910.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>2720229</th>\n",
       "      <td>4e0bd7c65da468f9bfb5939582358310</td>\n",
       "      <td>win8defender</td>\n",
       "      <td>1.1.15200.1</td>\n",
       "      <td>4.18.1807.18075</td>\n",
       "      <td>1.275.569.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50188.0</td>\n",
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       "      <td>6899.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>win8defender</td>\n",
       "      <td>1.1.15100.1</td>\n",
       "      <td>4.18.1807.18075</td>\n",
       "      <td>1.273.1616.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53447.0</td>\n",
       "      <td>...</td>\n",
       "      <td>63555.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1693213</th>\n",
       "      <td>30993d30aa1c94e4e75032884e4d99e2</td>\n",
       "      <td>win8defender</td>\n",
       "      <td>1.1.15100.1</td>\n",
       "      <td>4.18.1807.18075</td>\n",
       "      <td>1.273.778.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>53447.0</td>\n",
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       "      <td>19951.0</td>\n",
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       "<p>5 rows × 83 columns</p>\n",
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      ],
      "text/plain": [
       "                           MachineIdentifier   ProductName EngineVersion  \\\n",
       "Unnamed: 0                                                                 \n",
       "1713700     312ee21c55c435e926e8d697463cb3b0  win8defender   1.1.15200.1   \n",
       "2218958     3fab37ea700827f06691984cc03652eb  win8defender   1.1.15200.1   \n",
       "2720229     4e0bd7c65da468f9bfb5939582358310  win8defender   1.1.15200.1   \n",
       "4079822     75114848cf4f7e16a317ba8f15efd1b2  win8defender   1.1.15100.1   \n",
       "1693213     30993d30aa1c94e4e75032884e4d99e2  win8defender   1.1.15100.1   \n",
       "\n",
       "                 AppVersion  AvSigVersion  IsBeta  RtpStateBitfield  \\\n",
       "Unnamed: 0                                                            \n",
       "1713700     4.18.1806.18062   1.275.202.0       0               7.0   \n",
       "2218958     4.18.1807.18075  1.275.1141.0       0               7.0   \n",
       "2720229     4.18.1807.18075   1.275.569.0       0               7.0   \n",
       "4079822     4.18.1807.18075  1.273.1616.0       0               7.0   \n",
       "1693213     4.18.1807.18075   1.273.778.0       0               7.0   \n",
       "\n",
       "            IsSxsPassiveMode  DefaultBrowsersIdentifier  \\\n",
       "Unnamed: 0                                                \n",
       "1713700                    0                        NaN   \n",
       "2218958                    0                        NaN   \n",
       "2720229                    0                        NaN   \n",
       "4079822                    0                        NaN   \n",
       "1693213                    0                        NaN   \n",
       "\n",
       "            AVProductStatesIdentifier      ...       \\\n",
       "Unnamed: 0                                 ...        \n",
       "1713700                       53447.0      ...        \n",
       "2218958                       47380.0      ...        \n",
       "2720229                       50188.0      ...        \n",
       "4079822                       53447.0      ...        \n",
       "1693213                       53447.0      ...        \n",
       "\n",
       "            Census_FirmwareVersionIdentifier  Census_IsSecureBootEnabled  \\\n",
       "Unnamed: 0                                                                 \n",
       "1713700                              64689.0                           1   \n",
       "2218958                              51910.0                           0   \n",
       "2720229                               6899.0                           0   \n",
       "4079822                              63555.0                           1   \n",
       "1693213                              19951.0                           1   \n",
       "\n",
       "            Census_IsWIMBootEnabled  Census_IsVirtualDevice  \\\n",
       "Unnamed: 0                                                    \n",
       "1713700                         0.0                     0.0   \n",
       "2218958                         NaN                     0.0   \n",
       "2720229                         NaN                     0.0   \n",
       "4079822                         NaN                     0.0   \n",
       "1693213                         NaN                     0.0   \n",
       "\n",
       "            Census_IsTouchEnabled  Census_IsPenCapable  \\\n",
       "Unnamed: 0                                               \n",
       "1713700                         0                    0   \n",
       "2218958                         1                    0   \n",
       "2720229                         0                    0   \n",
       "4079822                         0                    0   \n",
       "1693213                         0                    0   \n",
       "\n",
       "            Census_IsAlwaysOnAlwaysConnectedCapable  Wdft_IsGamer  \\\n",
       "Unnamed: 0                                                          \n",
       "1713700                                         0.0           0.0   \n",
       "2218958                                         0.0           0.0   \n",
       "2720229                                         0.0           1.0   \n",
       "4079822                                         0.0           0.0   \n",
       "1693213                                         0.0           0.0   \n",
       "\n",
       "           Wdft_RegionIdentifier HasDetections  \n",
       "Unnamed: 0                                      \n",
       "1713700                      3.0             1  \n",
       "2218958                     15.0             1  \n",
       "2720229                      3.0             1  \n",
       "4079822                      3.0             1  \n",
       "1693213                      7.0             1  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each row in this dataset corresponds to a machine, uniquely identified by a MachineIdentifier. \n",
    "#### HasDetections is the ground truth and indicates that Malware was detected on the machine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let us look at the description of major attributes in the dataset  \n",
    "\n",
    "<li>MachineIdentifier - Individual machine ID\n",
    "<li>ProductName - Defender state information e.g. win8defender<br>\n",
    "<li>EngineVersion - Defender state information e.g. 1.1.12603.0<br>\n",
    "<li>AppVersion - Defender state information e.g. 4.9.10586.0<br>\n",
    "<li>AvSigVersion - Defender state information e.g. 1.217.1014.0<br>\n",
    "<li>AVProductStatesIdentifier - ID for the specific configuration of a user's antivirus software<br>\n",
    "<li>CountryIdentifier - ID for the country the machine is located in<br>\n",
    "<li>OsVer - Version of the current operating system<br>\n",
    "<li>OsPlatformSubRelease - Returns the OS Platform sub-release (Windows Vista, Windows 7, Windows 8, TH1, TH2)<br>\n",
    "<li>SmartScreen - This is the SmartScreen enabled string value from registry. If the value exists but is blank, the value \"ExistsNotSet\" is sent in telemetry.<br>\n",
    "<li>Firewall - This attribute is true (1) for Windows 8.1 and above if windows firewall is enabled, as reported by the service.\n",
    "<li>UacLuaenable - This attribute reports whether or not the \"administrator in Admin Approval Mode\" user type is disabled or enabled in UAC.\n",
    "<li>Census_ProcessorCoreCount - Number of logical cores in the processor<br>\n",
    "<li>Census_PrimaryDiskTotalCapacity - Amount of disk space on primary disk of the machine in MB<br>\n",
    "<li>Census_SystemVolumeTotalCapacity - The size of the partition that the System volume is installed on in MB<br>\n",
    "<li>Census_TotalPhysicalRAM - Retrieves the physical RAM in MB<br>\n",
    "<li>Census_GenuineStateName - Friendly name of OSGenuineStateID. 0 = Genuine<br>\n",
    "<li>Census_IsTouchEnabled - Is this a touch device ?<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It is a large dataset but let us see based on various attributes of a machince, can we predict if a machine will get hit by a malware? <br>Let us look at the distribution of outcome variable \n",
    "HasDetections is the ground truth and indicates that Malware was detected on the machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['HasDetections'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see we have a balanced dataset, that means dataset has been sampled to include a much larger proportion of malware machines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us create a function to have a look at the categorical attributes\n",
    "def plotbar(col):\n",
    "    plt.figure(figsize=(11,4))\n",
    "    g = sns.countplot(x= col, hue='HasDetections',data=train,palette=[\"C1\", \"C0\"],order = train[col].value_counts().iloc[:15].index)\n",
    "    g.set_xticklabels(g.get_xticklabels(),rotation=90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us have a look at the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotbar('Census_IsTouchEnabled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We can see that the detections are less in Touch devices.<br> The rate of infections is lower for touch devices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotbar('Census_OSVersion')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a significant difference in detection levels based on the Os versions.  <br> <b>Hence we can see that depending upon the attributes the risks of getting infected can change!</b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotbar('AVProductsInstalled')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the number of antivirus products installed. In case of a single antivirus, the rate of detection is high. \n",
    "Installing two antivirus products actually does not improve the chance of detection!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotbar('OsPlatformSubRelease')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RS indicates Redstone and th is threshold which are both versions of windows 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also we can see that rs4 has more number of detections this maybe as it was a new version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotbar('SmartScreen')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SmartScreen Filter helps to identify reported phishing and malware websites and also helps you make informed decisions about downloads. <br> As you browse the web, it analyzes pages and determines if they might be suspicious. <br> <br> If it finds a match, SmartScreen will show you a warning letting you know that the site has been blocked for your safety. <br>SmartScreen checks files that you download from the web against a list of reported malicious software sites and programs known to be unsafe.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the SmartScreen enabled string value from registry.  We can see that if it exists and is not set can have a large number of detections!\n",
    "#### We will see it is the most important feature in detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotbar('CountryIdentifier')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the detection levels differ a lot based on country. This could be a good feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us seperate categorical and numerical features. According to description of data, following are numerical columns\n",
    "numerical_columns = [\n",
    "    'Census_ProcessorCoreCount',\n",
    "    'Census_PrimaryDiskTotalCapacity',\n",
    "    'Census_SystemVolumeTotalCapacity',\n",
    "    'Census_TotalPhysicalRAM',\n",
    "    'Census_InternalPrimaryDiagonalDisplaySizeInInches',\n",
    "    'Census_InternalPrimaryDisplayResolutionHorizontal',\n",
    "    'Census_InternalPrimaryDisplayResolutionVertical',\n",
    "    'Census_InternalBatteryNumberOfCharges'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us seperate the attributes with binary values\n",
    "binary_columns = [i for i in train.columns if train[i].nunique() == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = [i for i in train.columns if (i not in binary_columns) and (i not in numerical_columns) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cardinality = []\n",
    "for i in categorical_columns:\n",
    "    if i == 'MachineIdentifier': continue\n",
    "    cardinality.append([i,train[i].nunique()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cardinality.sort(key = lambda x:x[1],reverse = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=[x[0] for x in cardinality]\n",
    "x=[x[1] for x in cardinality]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "g =sns.barplot(y[0:10],x[0:10])\n",
    "g.set_xticklabels(g.get_xticklabels(),rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see these columns have high cardinality, frequency encoding will ranking the categories with respect to their frequencies. These variables are then treated as numerical.<br> And we can then use them in our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let us perform encoding on these variables\n",
    "encoding_variables = [\n",
    "    'Census_OEMModelIdentifier',\n",
    "    'CityIdentifier',\n",
    "    'Census_FirmwareVersionIdentifier',\n",
    "    'AvSigVersion',\n",
    "    'Census_ProcessorModelIdentifier',\n",
    "    'Census_OEMNameIdentifier',\n",
    "    'DefaultBrowsersIdentifier'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frequency_encoding(variable):\n",
    "    t = pd.concat([train[variable], test[variable]]).value_counts().reset_index()\n",
    "    t = t.reset_index()\n",
    "    t.loc[t[variable] == 1, 'level_0'] = np.nan\n",
    "    t.set_index('index', inplace=True)\n",
    "    max_label = t['level_0'].max() + 1\n",
    "    t.fillna(max_label, inplace=True)\n",
    "    return t.to_dict()['level_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "for variable in encoding_variables:\n",
    "    freq_variables = frequency_encoding(variable)\n",
    "    train[variable] = train[variable].map(lambda x: freq_variables.get(x, np.nan))\n",
    "    test[variable] = test[variable].map(lambda x: freq_variables.get(x, np.nan))\n",
    "    categorical_columns.remove(variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "indexer = {}\n",
    "for col in categorical_columns:\n",
    "    if col == 'MachineIdentifier': continue\n",
    "    _, indexer[col] = pd.factorize(train[col])\n",
    "    \n",
    "for col in categorical_columns:\n",
    "    if col == 'MachineIdentifier': continue\n",
    "    train[col] = indexer[col].get_indexer(train[col])\n",
    "    test[col] = indexer[col].get_indexer(test[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = train['HasDetections']\n",
    "del train['HasDetections']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Now that we are done with feature engineering let us move to machine learning and prediction.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us make use of lgbm, Light GBM is a gradient boosting framework that uses tree based learning algorithm.\n",
    "<br> Light GBM is a fast, distributed, high-performance gradient boosting framework based on decision tree algorithm, used for ranking, classification and many other machine learning tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#These are the parameters that we would be setting for lgbm model\n",
    "param = {'num_leaves': 60,\n",
    "         'min_data_in_leaf': 60, \n",
    "         'objective':'binary',\n",
    "         'max_depth': -1,\n",
    "         'learning_rate': 0.1,\n",
    "         \"boosting\": \"gbdt\",\n",
    "         \"feature_fraction\": 0.8,\n",
    "         \"bagging_freq\": 1,\n",
    "         \"bagging_fraction\": 0.8 ,\n",
    "         \"bagging_seed\": 11,\n",
    "         \"metric\": 'auc',\n",
    "         \"lambda_l1\": 0.1,\n",
    "         \"random_state\": 133,\n",
    "         \"verbosity\": -1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_iter = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following are the cross validation scores for the training data inorder to create an optimized model with high accuracy. We made use of cross validation with 5 folds, the advantage of this method is that all observations are used for both training and validation, and each observation is used for validation exactly once. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold n°0\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's auc: 0.751319\tvalid_1's auc: 0.720071\n",
      "[200]\ttraining's auc: 0.769667\tvalid_1's auc: 0.721429\n",
      "[300]\ttraining's auc: 0.781697\tvalid_1's auc: 0.721092\n",
      "Early stopping, best iteration is:\n",
      "[185]\ttraining's auc: 0.767497\tvalid_1's auc: 0.721497\n",
      "time elapsed: 0.038s\n",
      "fold n°1\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's auc: 0.751309\tvalid_1's auc: 0.721836\n",
      "[200]\ttraining's auc: 0.769685\tvalid_1's auc: 0.722959\n",
      "[300]\ttraining's auc: 0.78145\tvalid_1's auc: 0.722416\n",
      "[400]\ttraining's auc: 0.791111\tvalid_1's auc: 0.721733\n",
      "Early stopping, best iteration is:\n",
      "[214]\ttraining's auc: 0.771747\tvalid_1's auc: 0.723126\n",
      "time elapsed: 0.079s\n",
      "fold n°2\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's auc: 0.751524\tvalid_1's auc: 0.720615\n",
      "[200]\ttraining's auc: 0.769441\tvalid_1's auc: 0.721717\n",
      "[300]\ttraining's auc: 0.781359\tvalid_1's auc: 0.721405\n",
      "[400]\ttraining's auc: 0.79129\tvalid_1's auc: 0.721\n",
      "Early stopping, best iteration is:\n",
      "[203]\ttraining's auc: 0.769872\tvalid_1's auc: 0.721767\n",
      "time elapsed: 0.12 s\n",
      "fold n°3\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's auc: 0.7513\tvalid_1's auc: 0.720716\n",
      "[200]\ttraining's auc: 0.769799\tvalid_1's auc: 0.721748\n",
      "[300]\ttraining's auc: 0.782024\tvalid_1's auc: 0.721542\n",
      "Early stopping, best iteration is:\n",
      "[190]\ttraining's auc: 0.768455\tvalid_1's auc: 0.72178\n",
      "time elapsed: 0.16 s\n",
      "fold n°4\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[100]\ttraining's auc: 0.751782\tvalid_1's auc: 0.722352\n",
      "[200]\ttraining's auc: 0.769942\tvalid_1's auc: 0.723223\n",
      "[300]\ttraining's auc: 0.781948\tvalid_1's auc: 0.722633\n",
      "[400]\ttraining's auc: 0.791381\tvalid_1's auc: 0.721827\n",
      "Early stopping, best iteration is:\n",
      "[202]\ttraining's auc: 0.770309\tvalid_1's auc: 0.72333\n",
      "time elapsed: 0.2  s\n",
      "CV score: 0.72230 \n"
     ]
    }
   ],
   "source": [
    "folds = KFold(n_splits=5, shuffle=True, random_state=15)\n",
    "oof = np.zeros(len(train))\n",
    "categorical_columns = [c for c in categorical_columns if c not in ['MachineIdentifier']]\n",
    "features = [c for c in train.columns if c not in ['MachineIdentifier']]\n",
    "predictions = np.zeros(len(test))\n",
    "start = time.time()\n",
    "feature_importance_df = pd.DataFrame()\n",
    "start_time= time.time()\n",
    "score = [0 for _ in range(folds.n_splits)]\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(train.values, target.values)):\n",
    "    print(\"fold n°{}\".format(fold_))\n",
    "    trn_data = lgb.Dataset(train.iloc[trn_idx][features],\n",
    "                           label=target.iloc[trn_idx],\n",
    "                           categorical_feature = categorical_columns\n",
    "                          )\n",
    "    val_data = lgb.Dataset(train.iloc[val_idx][features],\n",
    "                           label=target.iloc[val_idx],\n",
    "                           categorical_feature = categorical_columns\n",
    "                          )\n",
    "\n",
    "    num_round = 10000\n",
    "    clf = lgb.train(param,\n",
    "                    trn_data,\n",
    "                    num_round,\n",
    "                    valid_sets = [trn_data, val_data],\n",
    "                    verbose_eval=100,\n",
    "                    early_stopping_rounds = 200)\n",
    "    \n",
    "    oof[val_idx] = clf.predict(train.iloc[val_idx][features], num_iteration=clf.best_iteration)\n",
    "    \n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"feature\"] = features\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importance(importance_type='gain')\n",
    "    fold_importance_df[\"fold\"] = fold_ + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "\n",
    "    # we perform predictions by chunks\n",
    "    initial_idx = 0\n",
    "    chunk_size = 1000000\n",
    "    current_pred = np.zeros(len(test))\n",
    "    while initial_idx < test.shape[0]:\n",
    "        final_idx = min(initial_idx + chunk_size, test.shape[0])\n",
    "        idx = range(initial_idx, final_idx)\n",
    "        current_pred[idx] = clf.predict(test.iloc[idx][features], num_iteration=clf.best_iteration)\n",
    "        initial_idx = final_idx\n",
    "    predictions += current_pred / min(folds.n_splits, max_iter)\n",
    "   \n",
    "    print(\"time elapsed: {:<5.2}s\".format((time.time() - start_time) / 3600))\n",
    "    score[fold_] = metrics.roc_auc_score(target.iloc[val_idx], oof[val_idx])\n",
    "    if fold_ == max_iter - 1: break\n",
    "        \n",
    "if (folds.n_splits == max_iter):\n",
    "    print(\"CV score: {:<8.5f}\".format(metrics.roc_auc_score(target, oof)))\n",
    "else:\n",
    "     print(\"CV score: {:<8.5f}\".format(sum(score) / max_iter))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Features that were most useful for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = (feature_importance_df[[\"feature\", \"importance\"]]\n",
    "        .groupby(\"feature\")\n",
    "        .mean()\n",
    "        .sort_values(by=\"importance\", ascending=False)[:1000].index)\n",
    "\n",
    "best_features = feature_importance_df.loc[feature_importance_df.feature.isin(cols)][:50]\n",
    "\n",
    "plt.figure(figsize=(14,15))\n",
    "sns.barplot(x=\"importance\",\n",
    "            y=\"feature\",\n",
    "            data=best_features.sort_values(by=\"importance\",\n",
    "                                           ascending=False))\n",
    "plt.title('LightGBM Features (avg over folds)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> We got an accuracy of 68.1 % on test dataset after submitting on kaggle.</b> <br>\n",
    "These results represent machines and the risk of being affected based on their features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Machine</th>\n",
       "      <th>Risk of Infection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bbd32c3d6e6673dab113227a96ea1614</td>\n",
       "      <td>33.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e5544db56485780c556ae0faa49a0dda</td>\n",
       "      <td>46.1%</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7b5a256ef0a3e28f9cebfa6e05b1427a</td>\n",
       "      <td>42.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>772afbaa64c2169494d0146a614fafff</td>\n",
       "      <td>23.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>df1cc7ed605e68570ec5cecb2232f5c4</td>\n",
       "      <td>46.7%</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>e64a52d52410e58b173a770b99ea75e4</td>\n",
       "      <td>43.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23e15d0ab76ed2de40d423b10a691ec8</td>\n",
       "      <td>52.8%</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9da269745fc049f4ec790111aa7c42c8</td>\n",
       "      <td>19.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>d600da38bf735caad71cd5c9100c172f</td>\n",
       "      <td>39.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>75d6b6247d558385de5c6e9242d73715</td>\n",
       "      <td>25.7%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Machine Risk of Infection\n",
       "0  bbd32c3d6e6673dab113227a96ea1614             33.8%\n",
       "1  e5544db56485780c556ae0faa49a0dda             46.1%\n",
       "2  7b5a256ef0a3e28f9cebfa6e05b1427a             42.3%\n",
       "3  772afbaa64c2169494d0146a614fafff             23.3%\n",
       "4  df1cc7ed605e68570ec5cecb2232f5c4             46.7%\n",
       "5  e64a52d52410e58b173a770b99ea75e4             43.9%\n",
       "6  23e15d0ab76ed2de40d423b10a691ec8             52.8%\n",
       "7  9da269745fc049f4ec790111aa7c42c8             19.9%\n",
       "8  d600da38bf735caad71cd5c9100c172f             39.8%\n",
       "9  75d6b6247d558385de5c6e9242d73715             25.7%"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detection_df = pd.DataFrame({\"MachineIdentifier\": test[\"MachineIdentifier\"].values})\n",
    "detection_df[\"HasDetections\"] = (predictions * 100).round(1)\n",
    "detection_df['HasDetections'] = detection_df['HasDetections'].apply( lambda x : str(x) + '%')\n",
    "detection_df.columns = ['Machine', 'Risk of Infection']\n",
    "detection_df[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus we have performed analysis on the attributes of a machine and found the attributes that are most important for predicting the risk of infection. SmartScreen, CountryIdentifier, AVProductStatesIdentifier, AVProductsInstalled, EngineVersion are some of the most important features that were helpful for the prediction.<br>\n",
    "<br>\n",
    "<b>SmartScreen</b> was the most important feature and it helps to identify reported phishing and malware websites and also helps you make informed decisions about downloads. We have see from the analysis if it exists and is not set can have a large number of detections! <br> <br>\n",
    "CountryIdentifier indicates the source country of the machine and we can see that malware rates varies across countries and is a strong identifier. <br>\n",
    "AVProductsInstalled indicates the number of Antivirus products installed.<br> Appversion and EngineVersion indicates the version of Windows defender.   <br><br>\n",
    "This means if we have information about a machine then we can predict the chances of them getting infected and hence if the risks are high then we can take counter measures accordingly. <br> Hence we can see by making use of machine learning we can identify the risks of infection and prevent our machines from getting infected!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<b>Future scope</b> <br>\n",
    "More complex models using deep learning can be utilized which can help to improve the accuracy further. By making use of deep learning we can create a model that learns on its own by performing a task repeatedly, each time tweaking it a little to improve the outcome. "
   ]
  }
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